Road Crack Detection-Combined Dataset Object Detection Dataset and Pre-Trained Model by Road Crack Project

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Explore Dataset

Here are a few use cases for this project:

Pothole and Road Maintenance Scheduling: City municipalities can use the "Road Crack Detection-Combined Dataset" model to analyze and prioritize road maintenance efforts, addressing areas with the highest concentrations of cracks and potholes first to optimize resource allocation.
Autonomous Vehicle Navigation: By integrating the "Road Crack Detection-Combined Dataset" model into self-driving vehicles, the system could optimize navigation routes based on road conditions, selecting roads with fewer cracks and potholes to improve ride quality and enhance the vehicle's performance.
Road Quality Assessment for Cycling: Cycling apps could incorporate the model to analyze and rate roads based on crack and pothole prevalence, helping cyclists choose safer and more enjoyable routes.
Infrastructure Investment Analysis: Government agencies and private investors can use the data obtained from the model to evaluate infrastructure investment needs and make data-driven decisions about allocating funds for road repair and development.
Insurance Claims Analysis: Insurance companies may utilize the "Road Crack Detection-Combined Dataset" model to verify road conditions to evaluate the probability of potential accidents or vehicle damages and adjust their policies accordingly. This can help them effectively assess the external factors influencing road incidents.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            road-crack-detection-combined-dataset_dataset,
                            title = { Road Crack Detection-Combined Dataset Dataset },
                            type = { Open Source Dataset },
                            author = { Road Crack Project },
                            howpublished = { \url{ https://universe.roboflow.com/road-crack-project/road-crack-detection-combined-dataset } },
                            url = { https://universe.roboflow.com/road-crack-project/road-crack-detection-combined-dataset },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jan },
                            note = { visited on 2024-04-28 },
                            }